Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel method to reduce the magnitude of 4D CT artifacts by stitching two images with a data-driven regularization constrain, which helps preserve the local anatomy structures. Our method first computes an interface seam for the stitching in the overlapping region of the first image, which passes through the "smoothest" region, to reduce the structure complexity along the stitching interface. Then, we compute the displacements of the seam by matching the corresponding interface seam in the second image. We use sparse 3D features as the structure cues to guide the seam matching, in which a regularization term is incorporated to keep the structure consistency. The energy function is minimized by solving a multiple-label problem in Markov Random Fields with an anatomical structure preserving regularization term. The displacements are propagated to the rest of second image and the two image are stitched along the interface seams based on the computed displacement field. The method was tested on both simulated data and clinical 4D CT images. The experiments on simulated data demonstrated that the proposed method was able to reduce the landmark distance error on average from 2.9 mm to 1.3 mm, outperforming the registration-based method by about 55%. For clinical 4D CT image data, the image quality was evaluated by three medical experts, and all identified much fewer artifacts from the resulting images by our method than from those by the compared method. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CVPR.2011.5995561 | CVPR |
Keywords | Field | DocType |
energy function,anatomical structure preserving regularization term,image quality,ct image,computerised tomography,motion artifact reduction,novel method,registration-based method,anatomical structure,sparse 3d features,simulated data,4d ct images,ct image data,interface seam matching,markov processes,image reconstruction,feature extraction,local anatomy structures,markov random fields,structure-awareness,corresponding interface seam,multiple-label problem,4d ct artifacts magnitude reduction,data-driven regularization constraint,image stitching,feature guided motion artifact reduction,medical image processing,regularization term,computed tomography,topology,labeling,structural complexity,comparative method,bioinformatics,biomedical research | Iterative reconstruction,Computer vision,Displacement field,Image stitching,Random field,Pattern recognition,Computer science,Image quality,Feature extraction,Regularization (mathematics),Artificial intelligence,Landmark | Conference |
Volume | Issue | ISSN |
2011 | 1 | 1063-6919 |
ISBN | Citations | PageRank |
978-1-4577-0394-2 | 2 | 0.43 |
References | Authors | |
9 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongfeng Han | 1 | 72 | 8.10 |
| John Bayouth | 2 | 22 | 2.72 |
| Qi Song | 3 | 63 | 5.44 |
| Sudershan Bhatia | 4 | 5 | 0.93 |
| Milan Sonka | 5 | 231 | 49.15 |
| Xiaodong Wu | 6 | 859 | 77.06 |